Core Quantum Routines
•2 min read
There are a few routines that build the core of almost any quantum algorithm, inclduding quantum machine learning. Today, we start with the Quantum Fourier Transform.
Letters
The personal review on what's on Quantum Machine Learning
You can't just load classical data into qubits
•2 min read
From basis states to amplitude, angle, and block encodings, each approach solves a different bottleneck, but none is universally applicable. If you've ever wondered, "How do I actually get data into qubits?", then this is your starting point.
Does This Appear Alien?
•2 min read
When something new first appears, it often feels strange. Think about the first time you saw a quantum circuit diagram.
An unexpected moment of insight
•2 min read
Quantum circuits are more than just abstract math. They bridge theory and hardware by turning dense unitary matrices into structured recipes that real devices can execute. Therefore, they provide an essential layer of abstraction that makes quantum computation both understandable and practical.
The Top-Level Layer of Quantum Machine Learning
•2 min read
Quantum machine learning must demonstrate superior representation power or computational efficiency to be meaningful. So, discover the various ways in which such a fusion can succeed and why they are important for the future of this field.
Quantum Machine Learning is about the right balance
•2 min read
A comprehensive look at parameterized quantum circuits and an glimpse into my writing routine. What could these two things possibly have in common?
It's probably...
•2 min read
At the heart of modern science and machine learning lies a seemingly simple process: sampling from a probability distribution. Whether you're running a Bayesian model, training a generative network, or simulating molecules, the quality of your samples determines the quality of your predictions.
Beyond Factorization And Amplification
•2 min read
Eigenvalues determine everything from how a quantum system evolves to how a kernel method in quantum machine learning defines similarity between data points. Without a way to compute or transform them efficiently, the promise of quantum speedups in machine learning collapses into impractical theory.
From Code Snippets to Quantum Insights: A New Phase for PyQML
•2 min read
It is not enough to simply explain quantum machine learning. You have to see it in action. This is the first step toward a more interactive reading experience and uncovering the areas where quantum models could ultimately surpass their classical counterparts.
Misconceptions, misjudgments, and poor use of technology
•2 min read
Would you print out your emails just to be faster? Even if that means ignoring the potential of your shiny new inbox, and even if that inbox still needs fixing? That's the kind of trade-off I'm facing today. And that's exactly the problem we have to deal with in quantum machine learning.
Is It All But A Complex Puzzle?
•2 min read
Quantum Machine Learning is less like solving a flat puzzle and more like discovering pieces that twist into higher dimensions. Let's explore how to balance the big picture with the technical details. And why seeing both matters for making real progress.
Engineering, Research, or Something Else?
•2 min read
In Quantum Machine Learning, quality comes from grounding ideas in solid sources and clear methods. This reflection looks at how crediting, design science research, and the balance between engineering and discovery shape what it really means to work in the field.
Quantum Machine Learning Is The Future...
•2 min read
PyQML comes in a new design. It's meant to make reading a pleasure, not a chore. Keywords are linked, and definitions are available when you need them.